Asphaltene adsorption using green nanocomposites: Experimental study and adaptive neuro-fuzzy interference system modeling

Asphaltene deposition is a known problem that causes significant cost increases in the oil industry. Two bio-templated adsorbents, namely the NiO/ZSM-5 and NiO/AlPO-5 nanocomposites, were used as new green adsorbents to remove asphaltene from a model oil solution. Composite adsorbents were characterized by FTIR, BET, TEM and XRD analysis. Batch adsorption experiments were carried out as a function of D/C0 [(g)adsorbent/(mg/l)initial], pH, and temperature (K). Results showed that maximum adsorption is obtained at D/C0 = 0.072[g/(mg/l)] with a pH of 4.8 and a temperature of 298 K for NiO/ZSM-5 and D/C0 = 0.084[g/(mg/l)] with a pH of 3.4 and a temperature of 298 K for NiO/AlPO-5. In the experimental data, equilibrium adsorption models were introduced and their constants were calculated. The equilibrium adsorption data on NiO/ZSM-5 were well matched to the Freundlich model at 298 K and 325 K, and Temkin model at 342 K and 353 K. For the NiO/AlPO-5 adsorption data, the Temkin model was the best model showing strong adsorption interactions of asphaltene and adsorbent. The adaptive neuro-fuzzy interference system (ANFIS) was also used to model and predict the amount of asphaltene adsorbed by the proposed nanocomposites. ANFIS designed by triangular-shaped membership functions with three nodes and first-order polynomial Sugeno type FIS was the optimal structure and gave R2 = 0.9999 and R2 = 0.9996 for train and test data, respectively. Finally, Monte Carlo algorithm was used for sensitivity analysis on the input variables which is necessary for process optimization. Results demonstrated that D/C0, pH, and temperature have the highest effect on asphaltene removal by nanoparticles.